[Objective] This study proposes a scientific document retrieval method combining formula match and text ranking, which address the challenges from mathematical expressions.[Methods] First, we used the analysis algorithm for formula description structure to study the mathematical expressions. Then, we acquired formula structure information, and retrieved technical documents based on mathematical expressions. Meanwhile, we obtained the inquiry keywords and document word vectors with the help of word embedding model. Finally, we ranked the documents based on the similarity between the two word vectors[Results] The recall and precision scores of our new model were 0.77 and 0.63, which were 24.2% and 23.5% higher than those of the traditional scientific document retrieval methods.[Limitations] Our method only focuses on expressions in LaTeX format.[Conclusions] The proposed model combining formula and document keywords improves the performance of scitific document retrieval.
宰新宇,田学东. 基于公式描述结构和词嵌入的科技文档检索方法*[J]. 数据分析与知识发现, 2020, 4(1): 131-138.
Xinyu Zai,Xuedong Tian. Retrieving Scientific Documents with Formula Description Structure and Word Embedding. Data Analysis and Knowledge Discovery, 2020, 4(1): 131-138.
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